Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068142
Eslam Essa, Bassem A. Abdullah, A. Wahba
Indoor positioning has grasped great attention in recent years, a lot of research papers have been published in this field. However, there exists no technology that proves its efficacy in all different cases. This paper presents a complete study for indoor positioning with a proposed solution. the first stage of the study is a comparative analysis of several well-known technologies currently available for Indoor Positioning such as Wi- Fi, BLE or GPS. The second stage is based on the comparison's result of the first stage and decide to use Bluetooth Low Energy (BLE) in my solution. BLE is a very practical solution for indoor positioning systems (IPS) which offers acceptable accuracy and low-cost deployment. In this work, I use Received Signal Strength Indicator (RSSI) to estimate the distance between different devices. In the third stage, signal's RSSI are filtered to increase the accuracy of estimated distance using Kalman filter. Finally, trilateration method is used for estimation the position of devices based on the output of previous stages. Experimental results show that the proposed solution has promising results by achieving a positioning accuracy within 0.76 to 0.39m and average 0.53m.
{"title":"Improve Performance of Indoor Positioning System using BLE","authors":"Eslam Essa, Bassem A. Abdullah, A. Wahba","doi":"10.1109/ICCES48960.2019.9068142","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068142","url":null,"abstract":"Indoor positioning has grasped great attention in recent years, a lot of research papers have been published in this field. However, there exists no technology that proves its efficacy in all different cases. This paper presents a complete study for indoor positioning with a proposed solution. the first stage of the study is a comparative analysis of several well-known technologies currently available for Indoor Positioning such as Wi- Fi, BLE or GPS. The second stage is based on the comparison's result of the first stage and decide to use Bluetooth Low Energy (BLE) in my solution. BLE is a very practical solution for indoor positioning systems (IPS) which offers acceptable accuracy and low-cost deployment. In this work, I use Received Signal Strength Indicator (RSSI) to estimate the distance between different devices. In the third stage, signal's RSSI are filtered to increase the accuracy of estimated distance using Kalman filter. Finally, trilateration method is used for estimation the position of devices based on the output of previous stages. Experimental results show that the proposed solution has promising results by achieving a positioning accuracy within 0.76 to 0.39m and average 0.53m.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124658961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068165
Mohamed Ibrahim, M. Sobh, Ayman M. Bahaa-Eldin
In this manuscript, an enhanced vehicle identification system is proposed. Unlike passive traditional RFID based system, the proposal tackles several security and trust problems taking into consideration high speed moving vehicles. An error correction coding system with a very secure, light weight encryption is proposed to protect and provide correct results of the tag reading while the vehicle is moving up to 200 KM/h. The system is secure against forged reading s and the TAG itself is securely hardened.
{"title":"An Enhanced Secure System for Vehicle RFID based identification Secure RFID Solution using Authentication and error correction","authors":"Mohamed Ibrahim, M. Sobh, Ayman M. Bahaa-Eldin","doi":"10.1109/ICCES48960.2019.9068165","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068165","url":null,"abstract":"In this manuscript, an enhanced vehicle identification system is proposed. Unlike passive traditional RFID based system, the proposal tackles several security and trust problems taking into consideration high speed moving vehicles. An error correction coding system with a very secure, light weight encryption is proposed to protect and provide correct results of the tag reading while the vehicle is moving up to 200 KM/h. The system is secure against forged reading s and the TAG itself is securely hardened.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"290 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123108106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068173
S. Mohsen, A. Zekry, M. Abouelatta, Khaled Y. Youssef
One of the attractive solutions used for supplying low-power medical applications is the photovoltaic (PV) energy harvesting system. In this paper, the proposed PV energy harvesting system is composed of a photovoltaic panel, a DC-DC boost converter, a fixed resistive load and an analog control algorithm. This algorithm is designed based on the output load current. The algorithm is implemented using the multisim program. This algorithm is simple, efficient, low cost and low power consumption because it measures only the output current parameter and does not need multipliers. The power consumption of the proposed load is approximately 39.24 mW. Therefore, the expected working duration of the load is 20.9 hours under continuously operation of the light for 4 hours. Finally, the simulation results illustrate the transient characteristics of the proposed PV system.
{"title":"Analog Control Algorithm-Based a Photovoltaic Energy Harvesting System for Low-Power Medical Applications","authors":"S. Mohsen, A. Zekry, M. Abouelatta, Khaled Y. Youssef","doi":"10.1109/ICCES48960.2019.9068173","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068173","url":null,"abstract":"One of the attractive solutions used for supplying low-power medical applications is the photovoltaic (PV) energy harvesting system. In this paper, the proposed PV energy harvesting system is composed of a photovoltaic panel, a DC-DC boost converter, a fixed resistive load and an analog control algorithm. This algorithm is designed based on the output load current. The algorithm is implemented using the multisim program. This algorithm is simple, efficient, low cost and low power consumption because it measures only the output current parameter and does not need multipliers. The power consumption of the proposed load is approximately 39.24 mW. Therefore, the expected working duration of the load is 20.9 hours under continuously operation of the light for 4 hours. Finally, the simulation results illustrate the transient characteristics of the proposed PV system.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116715686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068184
Mohamed Awni, M. Khalil, Hazem M. Abbas
In recent years, ensemble learning methods show great effectiveness in improving model performance in several applications. Ensemble techniques rely on the incorporation of multiple different models together to get one optimal model. The primary assumption of ensemble techniques is that the cooperation among various classifiers will probably compensate for the mistakes of a single classifier and consequently, the ensemble's general output prediction would be better than the prediction of a single classifier. A key issue in the combination of classifiers is the diversity among its members. In this paper, we utilized model averaging as an ensemble learning technique for offline Arabic handwritten word recognition to train three residual networks (ResNet18) models. We demonstrate improvements by incorporating diversity in output prediction by using distinct techniques of optimization. To validate the proposed method, experiments have been carried on the IFN/ENIT (v2.0ple) database which contains 32,492 handwritten Arabic words of 937 unique Arabic words.
{"title":"Deep-Learning Ensemble for Offline Arabic Handwritten Words Recognition","authors":"Mohamed Awni, M. Khalil, Hazem M. Abbas","doi":"10.1109/ICCES48960.2019.9068184","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068184","url":null,"abstract":"In recent years, ensemble learning methods show great effectiveness in improving model performance in several applications. Ensemble techniques rely on the incorporation of multiple different models together to get one optimal model. The primary assumption of ensemble techniques is that the cooperation among various classifiers will probably compensate for the mistakes of a single classifier and consequently, the ensemble's general output prediction would be better than the prediction of a single classifier. A key issue in the combination of classifiers is the diversity among its members. In this paper, we utilized model averaging as an ensemble learning technique for offline Arabic handwritten word recognition to train three residual networks (ResNet18) models. We demonstrate improvements by incorporating diversity in output prediction by using distinct techniques of optimization. To validate the proposed method, experiments have been carried on the IFN/ENIT (v2.0ple) database which contains 32,492 handwritten Arabic words of 937 unique Arabic words.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129172152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068148
Heba Sayed, Georgios A. Dafoulas
The proliferation of Social Network Sites (SNS) first appeared globally during 1997 serving intrinsic social communication through SixDegrees.com. However, SNS use was heavily acknowledged in the Middle East and North Africa (MENA) region from 2011 onwards with the rise of the Arab Spring. SNS affect values, beliefs and perceptions of people using them, which are reflected on their attitudes and behaviors towards their surroundings. This paper discusses SNS users' behavioral typology within the context of Egypt's society. The study presented in this paper was based on a survey of 300 participants covering a wide demographic spectrum. The analysis process utilized quantitative and qualitative techniques. The findings of this study contribute towards the development of SNS users' behavioral typology in Egypt after nearly ten years of SNS usage.
{"title":"Social Network Sites (SNS) Users Behavioral Typology in Egypt","authors":"Heba Sayed, Georgios A. Dafoulas","doi":"10.1109/ICCES48960.2019.9068148","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068148","url":null,"abstract":"The proliferation of Social Network Sites (SNS) first appeared globally during 1997 serving intrinsic social communication through SixDegrees.com. However, SNS use was heavily acknowledged in the Middle East and North Africa (MENA) region from 2011 onwards with the rise of the Arab Spring. SNS affect values, beliefs and perceptions of people using them, which are reflected on their attitudes and behaviors towards their surroundings. This paper discusses SNS users' behavioral typology within the context of Egypt's society. The study presented in this paper was based on a survey of 300 participants covering a wide demographic spectrum. The analysis process utilized quantitative and qualitative techniques. The findings of this study contribute towards the development of SNS users' behavioral typology in Egypt after nearly ten years of SNS usage.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"05 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128225885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068188
Eman AbdelMaksoud, S. Barakat, Mohammed M Elmogy
Multi-label classification (MLC) is considered an active research topic, especially in medical image analysis. We used MLC to diagnose the multiple different grades of Diabetic Retinopathy (DR). DR is caused when a patient's blood pressure and blood sugar are too high and results in damage in the blood vessels (BVs). BVs supply the blood to the retina. If the retina does not get the blood it needs, it can eventually cause permanent blindness. The early diagnosis of different DR grades leads the ophthalmologists to efficient treatment. In this paper, we developed a multi-label computer-aided diagnosis (ML-CAD) system to apply MLC for different DR grades using color fundus images. Our system utilizes 11 texture features descriptors by retrieving the average of the Gray Level Run Length Matrix (GLRM) on four directions 0°, 45°, 90°, and 135°. It distinguishes the normal from DR cases by supplying the extracted features to the support vector machine (SVM) classifier. Then, the proposed CAD system segments some significant characteristics from DR fundus images, which are BV, exudates (EX), microaneurysms (MA), and hemorrhages (HM). After that, it calculates the Gray Level Co-occurrence Matrix (GLCM), regions of interest (ROIs) areas, and BV bifurcation point's calculations. Finally, the feature vector is trained and tested using a multi-label SVM (MSVM) classifier generates a suitable DR grade. We used four various benchmark datasets to evaluate the performance of our system in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), the area under the curve (AVC), and micro F1 measure. The experiments confirmed that our ML-CAD system outperforms the other diagnosing DR systems.
{"title":"A Multi-Label Computer-aided Diagnoses System for Detecting and Diagnosing Diabetic Retinopathy","authors":"Eman AbdelMaksoud, S. Barakat, Mohammed M Elmogy","doi":"10.1109/ICCES48960.2019.9068188","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068188","url":null,"abstract":"Multi-label classification (MLC) is considered an active research topic, especially in medical image analysis. We used MLC to diagnose the multiple different grades of Diabetic Retinopathy (DR). DR is caused when a patient's blood pressure and blood sugar are too high and results in damage in the blood vessels (BVs). BVs supply the blood to the retina. If the retina does not get the blood it needs, it can eventually cause permanent blindness. The early diagnosis of different DR grades leads the ophthalmologists to efficient treatment. In this paper, we developed a multi-label computer-aided diagnosis (ML-CAD) system to apply MLC for different DR grades using color fundus images. Our system utilizes 11 texture features descriptors by retrieving the average of the Gray Level Run Length Matrix (GLRM) on four directions 0°, 45°, 90°, and 135°. It distinguishes the normal from DR cases by supplying the extracted features to the support vector machine (SVM) classifier. Then, the proposed CAD system segments some significant characteristics from DR fundus images, which are BV, exudates (EX), microaneurysms (MA), and hemorrhages (HM). After that, it calculates the Gray Level Co-occurrence Matrix (GLCM), regions of interest (ROIs) areas, and BV bifurcation point's calculations. Finally, the feature vector is trained and tested using a multi-label SVM (MSVM) classifier generates a suitable DR grade. We used four various benchmark datasets to evaluate the performance of our system in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), the area under the curve (AVC), and micro F1 measure. The experiments confirmed that our ML-CAD system outperforms the other diagnosing DR systems.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129538768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/icces48960.2019.9068126
{"title":"Session PC: Parallel and Cloud Computing","authors":"","doi":"10.1109/icces48960.2019.9068126","DOIUrl":"https://doi.org/10.1109/icces48960.2019.9068126","url":null,"abstract":"","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114625064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068155
Mohamed M. Ahmed, Hassan Bedour, S. M. Hassan
the onboard radar is emerging as one of the most practical methods that can be used for identification and many other applications. Nowadays, almost all unmanned applications use onboard radar as a main sensor that provides critical information. Airplanes, satellites and some unmanned vehicles use various kinds of radars sensors depending on the required mission. Imaging radar sensor is used to produce two-dimensional images. It produces its light to illuminate certain area and take a picture at radio wavelengths. This kind of radars produces high quality images with large size. Thus; the produced images must be compressed to reduce their size and decompressed when used. There are different algorithms for compression and decompression, but when onboard, there is a need for an algorithm that will not consume excessive power to save batteries and require less time to be reliable for use. This paper discloses a new methodology for the image compression based upon the compressive sensing techniques., its implementation using the FPGA and the required simulation.
{"title":"FPGA Implementation of an ImageCompression and Reconstruction System for the Onboard Radar Using the Compressive Sensing","authors":"Mohamed M. Ahmed, Hassan Bedour, S. M. Hassan","doi":"10.1109/ICCES48960.2019.9068155","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068155","url":null,"abstract":"the onboard radar is emerging as one of the most practical methods that can be used for identification and many other applications. Nowadays, almost all unmanned applications use onboard radar as a main sensor that provides critical information. Airplanes, satellites and some unmanned vehicles use various kinds of radars sensors depending on the required mission. Imaging radar sensor is used to produce two-dimensional images. It produces its light to illuminate certain area and take a picture at radio wavelengths. This kind of radars produces high quality images with large size. Thus; the produced images must be compressed to reduce their size and decompressed when used. There are different algorithms for compression and decompression, but when onboard, there is a need for an algorithm that will not consume excessive power to save batteries and require less time to be reliable for use. This paper discloses a new methodology for the image compression based upon the compressive sensing techniques., its implementation using the FPGA and the required simulation.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115026237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/icces48960.2019.9068139
{"title":"Session CNS: Computer Networks & Security","authors":"","doi":"10.1109/icces48960.2019.9068139","DOIUrl":"https://doi.org/10.1109/icces48960.2019.9068139","url":null,"abstract":"","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"275 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116557572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-12-01DOI: 10.1109/ICCES48960.2019.9068153
Sara Salama, Rashed K. Salem, H. Abdel-Kader
Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.
{"title":"Improving Golay Code Using Hashing Technique","authors":"Sara Salama, Rashed K. Salem, H. Abdel-Kader","doi":"10.1109/ICCES48960.2019.9068153","DOIUrl":"https://doi.org/10.1109/ICCES48960.2019.9068153","url":null,"abstract":"Data are the representation of our world and our life. Data are increasing continuously, they come from different sources such as sensors, maps, climate informatics, smartphones, social media and/or medical data domains. Data are represented by different forms such as image, text, video and/or digital data. These incomprehensible data need an influential technique to be clustered and analyzed. This paper presents a hashing technique for the clustering process of unclassified and disorganized data. These clustered data are useful for decision-making process. The proposed technique is based on Golay error-correction code. The main concept is reversing the original Golay error-correction scheme and building Golay Code Addresses Hash Table (GCAHT). Simulation results stated that the proposed technique achieved high performance. Beta-CV, Dunn Index, C-index and Sum Square Error are used for measurements.","PeriodicalId":136643,"journal":{"name":"2019 14th International Conference on Computer Engineering and Systems (ICCES)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114197561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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